Standard Network Analysis: agent x location

Standard Network Analysis: agent x location

Input data: agent x location

Start time: Thu Nov 17 13:54:45 2011

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Network Level Measures

MeasureValue
Row count16.000
Column count7.000
Link count33.000
Density0.295

Node Level Measures

MeasureMinMaxAvgStddev
In-degree centrality0.0210.2710.1340.082
In-degree centrality [Unscaled]1.00013.0006.4293.923
Out-degree centrality0.0480.6190.1340.146
Out-degree centrality [Unscaled]1.00013.0002.8133.066

Key Nodes

In-degree centrality

The In Degree Centrality of a node is its normalized in-degree. For any node, e.g. an individual or a resource, the in-links are the connections that the node of interest receives from other nodes. For example, imagine an agent by knowledge matrix then the number of in-links a piece of knowledge has is the number of agents that are connected to. The scientific name of this measure is in-degree and it is calculated on the agent by agent matrices.

Input network(s): agent x location

RankLocationValueUnscaled
1revanna0.27113.000
2earth0.22911.000
3yu_homeworld0.1256.000
4tollana0.1256.000
5hasaraSystem_spaceStation0.1045.000
6cargoShip_tok'ra0.0633.000
7cargoShip_spaceStation0.0211.000

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Out-degree centrality

For any node, e.g. an individual or a resource, the out-links are the connections that the node of interest sends to other nodes. For example, imagine an agent by knowledge matrix then the number of out-links an agent would have is the number of pieces of knowledge it is connected to. The scientific name of this measure is out-degree and it is calculated on the agent by agent matrices. Individuals or organizations who are high in most knowledge have more expertise or are associated with more types of knowledge than are others. If no sub-network connecting agents to knowledge exists, then this measure will not be calculated. The scientific name of this measure is out degree centrality and it is calculated on agent by knowledge matrices. Individuals or organizations who are high in "most resources" have more resources or are associated with more types of resources than are others. If no sub-network connecting agents to resources exists, then this measure will not be calculated. The scientific name of this measure is out degree centrality and it is calculated on agent by resource matrices.

Input network(s): agent x location

RankAgentValueUnscaled
1daniel_jackson0.61913.000
2jacob_carter_selmak0.3337.000
3ren'al0.1904.000
4col_jack_o'neill0.1433.000
5maj_samantha_carter0.1433.000
6teal'c0.1433.000
7lt_elliott0.0952.000
8maj_mansfield0.0952.000
9aldwin0.0481.000
10janet_frazier0.0481.000

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Key Nodes Table

This shows the top scoring nodes side-by-side for selected measures.

RankBetweenness centralityCloseness centralityEigenvector centralityEigenvector centrality per componentIn-degree centralityIn-Closeness centralityOut-degree centralityTotal degree centrality
1----revanna-daniel_jackson-
2----earth-jacob_carter_selmak-
3----yu_homeworld-ren'al-
4----tollana-col_jack_o'neill-
5----hasaraSystem_spaceStation-maj_samantha_carter-
6----cargoShip_tok'ra-teal'c-
7----cargoShip_spaceStation-lt_elliott-
8------maj_mansfield-
9------aldwin-
10------janet_frazier-